Bibliometric analysis of the 50 most cited articles on artificial intelligence for lung cancer imaging

Year 2023, Volume: 6 Issue: 3, 686 - 692, 31.05.2023

Mehmet Serindere

https://doi.org/10.32322/jhsm.1294551

Abstract

Aim: The use of machine learning has now become widespread in lung cancer. However, the research trend is still unclear. This study aimed to analyze the most influential publications on artificial intelligence (AI) for lung cancer.
Material and Method: A comprehensive PubMed and SCImago Journal and Country Rank (SJR) search was performed. The 50 most cited articles were recorded according to the citation numbers, the country and institute of articles, the name and metrics of the publishing journal, the year of publication, and the content of the articles.
Results: The citation numbers ranged from 24 to 628. Annual citations per article was between 1.47 and 104.6. The USA was the country with the most publications (n=22) followed by The Netherlands (n=9) and Peoples R China (n=5). The journal and institution that highly contributed to the 50 most cited articles were Radiology (n=5) and Harvard Medical School (n=5), respectively.
Conclusion: The importance of deep learning and AI in lung cancer imaging is increasing day by day. In this study, a detailed bibliometric analysis of the literature on AI in lung cancer imaging was performed. In addition, this bibliometric analysis informs researchers about current influential papers in this field, the characteristics of these studies, and potential future trends in the rapidly evolving field of AI in lung cancer screening.

Keywords

Artificial intelligence, bibliometric analysis, lung cancer

References

• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7–34.
• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70: 7-30. 
• Richards TB, Henley SJ, Puckett MC, et al. Lung cancer survival in the United States by race and stage (2001-2009): Findings from the CONCORD-2 study. Cancer 2017;123: 5079-99.
• Li N, Wang L, Hu Y, et al. Global evolution of research on pulmonary nodules: a bibliometric analysis. Future Oncol 2021;17: 2631-45.
• Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health 2020; 2: e116-e128.
• Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinformatics 2022; 20: 850-66.
• Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 2020; 13: 6–19.
• Yanase J, Triantaphyllou E. A systematic survey of computeraided diagnosis in medicine: past and present developments. Expert Syst Appl 2019; 138: 112821.
• Abe Y, Hanai K, Nakano M, et al. A computer-aided diagnosis (CAD) system in lung cancer screening with computed tomography. Anticancer Res 2005; 25: 483–8.
• Mohammad BA, Brennan PC, Mello-Thoms C. A review of lung cancer screening and the role of computer-aided detection. Clin Radiol 2017; 72: 433–42.
• Armato 3rd SG, Li F, Giger ML, MacMahon H, Sone S, Doi K. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 2002; 225: 685–92.
• Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of artificial intelligence in screening, diagnosis, treatment, and prognosis of colorectal cancer. Curr Oncol 2022; 29: 1773-95.
• Karger E, Kureljusic M. Artificial intelligence for cancer detection-a bibliometric analysis and avenues for future research. Curr Oncol 2023; 30: 1626-47.
• Oo AM, ChuT TS. Bibliometric analysis of the top 100 cited articles in head and neck radiology. Acta Radiol Open 2021; 10: 20584601211001815.
• Sreedharan S, Mian M, Robertson RA, Yang N. The top 100 most cited articles in medical artificial intelligence: a bibliometric analysis. J Med Artif Intell 2020; 3: 3.
• Cheek J, Garnham B, Quan J. What’s in a number? Issues in providing evidence of impact and quality of research(ers). Qual Health Res 2006; 16: 423-35.
• pubmeddev. Pubmed. National Library of Medicine (US). Available online: https://www.ncbi.nlm.nih.gov/pubmed/ (accessed on 20 March 2023)
• Scimago Journal & Country Rank. https://www.scimagojr.com/ (accessed on 20 March 2023)
• Journal Citation Reports - Home. https://jcr.clarivate.com/jcr/home (accessed on 20 March 2023)
• Hughes H, O'Reilly M, McVeigh N, Ryan R. The top 100 most cited articles on artificial intelligence in radiology: a bibliometric analysis. Clin Radiol 2023; 78: 99-106.
• Zhang J, Zhu H, Wang J, et al. Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis. Front Oncol 2023; 13: 1082423.
• Czajbók E, Berhidi A, Vasas L, Schubert A. Hirsch-index for countries based on Essential Science Indicators data. Scientometrics 2007; 73: 91-117.
• Özdemir Ö, Boyalı O. Meningioma: a bibliometric analysis of the 50 most cited articles. Med J Bakirkoy 2023; 19: 71-7.
• Liang H, Chen Z, Wei F, Yang R, Zhou H. Bibliometrics research on radiomics of lung cancer. Transl Cancer Res 2021; 10: 3757-71.
• Hillner BE, Tosteson AN, Song Y, et al. Growth in the use of PET for six cancer types after coverage by medicare: additive or replacement? J Am Coll Radiol 2012; 9: 33–41.
• de Galiza Barbosa F, Delso G, Ter Voert EE, Huellner MW, Herrmann K, Veit- Haibach P. Multi-technique hybrid imaging in PET/CT and PET/MR: what does the future hold? Clin Radiol 2016; 71: 660–72.
• Slomka PJ, Pan T, Germano G. Recent advances and future progress in PET instrumentation. Semin Nucl Med 2016; 46: 5–19.
• Yaxley KL, To MS. The 100 top-cited meta-analyses of diagnostic accuracy in radiology journals: a bibliometric analysis. Insights Imaging 2020; 11: 123.